Detection and identification of viruses by electron microscopy

Electron microscopy can aid in the rapid diagnosis of viral diseases, as it can be performed in a matter of hours, but on a routine basis it should be used in conjunction with other techniques. Initially, the specimen source and patient symptoms should be ascertained, as these will lend suggestions of possible agents while eliminating others; however, this information should not be allowed to prejudice observation in such a way as to cause oversight of an unlikely pathogen. Second, selection of the method of preparation should be based on sample consistency; extraction, debris clarification, concentration, tissue culture amplification, or embedment may be necessary. Finally, false-positive results must be avoided by differentiating viruses from cell organelles or debris, mycoplasmal or bacterial contamination, and bacteriophages.     

如何提高SMT设备运行效率

SMT设备运行效率的主要指标有贴装正确率与故障停机率，如何保持和提高SMT设备贴装正确率及降低故障停机率，以创造最大限度的效益，是设备在使用过程中所面临的首要课题，以松下高速贴片机为例，从多方面详细阐述影响设备贴装率的原因，并详细介绍了SMT设备故障的诊断方法及其常见故障的解决方案。     

Clinical psychologists' theory-based representations of mental disorders predict their diagnostic reasoning and memory.

The theory-based model of categorization posits that concepts are represented as theories, not feature lists. Thus, it is interesting that the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV, American Psychiatric Association, 1994) established atheoretical guidelines for mental disorder diagnosis. Five experiments investigated how clinicians handled an atheoretical nosology. Clinicians' causal theories of disorders and their responses on diagnostic and memory tasks were measured. Participants were more likely to diagnose a hypothetical patient with a disorder if that patient had causally central rather than causally peripheral symptoms according to their theory of the disorder. Their memory for causally central symptoms was also biased. Clinicians are cognitively driven to use theories despite decades of practice with the atheoretical DSM. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The design of a knowledge based process diagnostic system

This paper describes the need for and the design of, a knowledge based process diagnostic system. A prototype system is implemented using Intellicorp's Knowledge Engineering Environment (KEE). The system is being implemented at a planned new assembly plant.     

Identifying persistently antisocial offenders using the Hare Psychopathy Checklist and DSM antisocial personality disorder criteria.

Early starting, lifetime criminal persistence has been called sociopathy, antisocial personality disorder, and psychopathy. There is, however, disagreement about its core features and which measure is best for identifying such individuals. In the 1st of 2 studies of male offenders (n = 74), we found a large association between scores on the Psychopathy Checklist-Revised (PCL-R; R. D. Hare, 1991) and the Diagnostic and Statistical Manual of Mental Disorders (4th ed. [DSM-IV; American Psychiatric Association, 1994) antisocial personality disorder criteria scored as a scale. The second study (n = 684) replicated this finding and found that, as previously shown for PCL-R scores, a discrete class (or taxon) also underlies scores on items reflecting antisocial personality disorder. The high association among these sets of items and their similarity in predicting violence suggested that the same natural class underlies each. Results indicated that life-course-persistent antisociality can be assessed well by measures of psychopathy and antisocial personality disorder. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Estimation of fuel cell operating time for predictive maintenance strategies

Durability is one of the limiting factors for spreading and commercialization of fuel cell technology. That is why research to extend fuel cell durability is being conducted world wide. A pattern-recognition approach aiming to estimate fuel cell operating time based on electrochemical impedance spectroscopy measurements is presented here. It is based on extracting the features from the impedance spectra. For that purpose, two approaches have been investigated. In the first one, particular points of the spectrum are empirically extracted as features. In the second approach, a parametric modeling is performed to extract features from both the real and the imaginary parts of the impedance spectrum. In particular, a latent regression model is used to automatically split the spectrum into several segments that are approximated by polynomials. The number of segments is adjusted taking into account the a priori knowledge about the physical behavior of the fuel cell components. Then, a linear regression model using different subsets of extracted features is employed for an estimate of the fuel cell operating time. The effectiveness of the proposed approach is evaluated on an experimental dataset. Allowing the estimation of the fuel cell operating time, and consequently its remaining duration life, these results could lead to interesting perspectives for predictive fuel cells maintenance policy.     

Fuel cells static and dynamic characterizations as tools for the estimation of their ageing time

This paper deals with a pattern-recognition-based diagnosis approach, which aim is to estimate the Fuel Cell (FC) operating time, and consequently its remaining duration life. With the method proposed, both static and dynamic information extracted from the stack (i.e. polarization curve records and Electrochemical Impedance Spectroscopy (EIS) measurements) can be used. The complete diagnosis method consists of several steps. First, features are extracted from EIS measurements and polarization curves independently. This enables us to simplify the extracted information without losing relevant information, and to remove noise. For the polarization curves, an empiric model is exploited to ensure the feature extraction. For the impedance spectra, both expert knowledge and parametric modeling are used to extract features. In particular, a latent regression model is used to split automatically the imaginary part of the spectra into several segments that are approximated by polynomials. The next step of the method consists in selecting the most relevant features from the whole set of extracted features. This helps us to estimate the operating time, while adjusting the complexity of the model. The final step of the approach is a linear regression that uses the selected subset of features to estimate the FC operating time. The performances of the proposed approach are evaluated on a dataset made up of EIS measurements and polarization curves extracted from two FC lifetime tests. A mean error of about 2 h over a global operating duration of 1000 h can be obtained. Moreover, the portability of the method is shown by considering another FC ageing test conducted on a different FC stack type.     

Diagnostic test decision for suppliers in cruise-building supply chain with information asymmetry

To solve the potential supply disruption in cruise-building supply chain, this study explores a supply system consisting of a monopoly supplier, a new entrant supplier, and a cruise-building manufacturer. A two-step option contract including diagnostic test and process improvement is designed to solve the order quantity allocation and to avoid supply disruption. The analysis combines the decision tree with the principal-agent model under information asymmetry. The result shows that the cruise-building manufacturer would like to allocate more orders to the unreliable supplier who implements process improvement. In the end, the detailed numerical analysis is implemented to justify the proposal.     

Using a failure modes, effects and diagnostic analysis (FMEDA) to measure diagnostic coverage in programmable electronic systems

One of the key issues in the quantitative evaluation of programmable electronic systems is the diagnostic capability of the equipment. This is measured by a parameter called the Coverage Factor, C. This factor can vary widely. The range of possible values is often the subject of great debate. Within limits, the diagnostic coverage factor can be calculated by knowing which component failure modes are detected by diagnostics. An extension of the Failure Modes and Effects Analysis (FMEA) can be used to show this information. This extension, called a Failure Modes, Effects and Diagnostic Analysis can serve as a useful design verification tool as well as a means to provide more precise input to reliability and safety modeling.     

Developing a diagnostic system through the integration of ant colony optimization systems and case-based reasoning

This study is dedicated to integrating both the clustering method and case-based reasoning (CBR) for developing a diagnostic system in maintenance. The reason for this is that searching similar cases for CBR is time consuming if the case base is fairly large. It is necessary to cluster the cases into some groups, and then perform the search for the most appropriate possible group. A novel approach, the ant colony system clustering algorithm (ASCA), is employed for this purpose. The main advantage of this technique is the reduction in the amount of time used in comparison. A real-life problem for car maintenance has shown evidence of this advantage as well as its precision ability.